There is growing evidence that the prenatal environment plays a role in the etiology of childhood asthma. Epidemiologic evidence suggests that children who develop asthma by school age already have 40% of their associated lung deficit at birth, and experimental studies in animals demonstrate that prenatal exposures to tobacco smoke, diesel exhaust, and other combustion-related particles can induce asthma in the offspring. The proposed research seeks to investigate the effect of exposure to ambient air pollution from traffic emissions during pregnancy on asthma incidence in childhood. We will study a well-characterized historical birth cohort of 19,169 mother-child pairs from Kaiser Permanente Georgia, a population with a high burden of asthma currently being studied as part of the Southeastern Center for Air Pollution and Epidemiology (SCAPE), an EPA-funded Clean Air Research Center. Daily spatially-resolved concentrations of nitrogen oxides (NOx), carbon monoxide (CO), particulate matter <2.5 micrometers in diameter (PM2.5), and PM2.5 elemental carbon will be assigned to each maternal address using a novel Bayesian hierarchical approach recently developed by SCAPE researchers. Calibrated Community Multi-scale Air Quality Model (CMAQ) simulations at 4 kilometer grid resolution are downscaled to 250 meter grid resolution using a Bayesian space- time downscaler model that incorporates additional fine-scale traffic emissions data, land-use information and meteorology. The unified approach enables the propagation of exposure estimation uncertainty from all sources through the epidemiologic models. Using comprehensive longitudinal medical histories on the children in the cohort we will assess prenatal concentrations of traffic pollutants in relation to incident asthma by 2, 4, 6, and 8 years of age, including subanalyses restricted to asthma cases with evidence of continued morbidity at each follow-up age. We will also estimate the effect of cumulative exposures for the prenatal period through the first year of life taking into account possible synergistic effects between exposure windows; estimate the joint effects of multiple traffic-related pollutants; and conduct an in-depth assessment of confounding by individual-level and contextual socioeconomic factors. Our access to complete maternal residence information during the prenatal period will allow us to characterize patterns of residential mobility during pregnancy for a large contemporary U.S. cohort and estimate impacts of this mobility on exposure estimation. These results will be relevant to the design and interpretation of a broad range of epidemiologic studies relying on residence location at the time of birth to assign spatially-varying exposures during pregnancy. By leveraging previously collected health data and novel air quality models that integrate multiple sources of air quality information we will be able to efficiently investigae the study questions and advance our understanding of modifiable risk factors for asthma, the most common chronic disease of childhood.